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Schnebele Gmu 0883E 10495.Pdf Fusion of Remote Sensing and Non-authoritative Data for Flood Disaster and Transportation Infrastructure Assessment A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University By Emily K. Schnebele Master of Arts University of Maryland at College Park, 1994 Bachelor of Science University of Maryland at College Park, 1992 Director: Dr. Guido Cervone, Associate Professor Department of Geography and GeoInformation Science Fall Semester 2013 George Mason University Fairfax, VA Copyright c 2013 by Emily K. Schnebele All Rights Reserved ii Acknowledgments Although I am listed as the solitary author of this work, its completion would not have been possible without the insight, guidance, and encouragement from my advisor, Dr. Guido Cervone. I have been privileged to have such a talented scientist and researcher as my advisor and mentor. No matter how challenging the task, from technical help to moral support, he always came to my aid. I have truly enjoyed being his student and I am eternally grateful for everything he has done for me. I also would like to thank the members of my committee, Dr. Nigel Waters, Dr. Richard Medina, and Dr. Monica Gentili. I have been extremely fortunate to have a committee who cares so much about me and my work. Without fail, they responded to my questions and requests for help by providing insightful and valuable advice. In particular, I would like to thank Dr. Waters for hiring me as his Research Assistant; the experience has been invaluable and it has been a pleasure to work alongside such a generous and talented professor. Dr. Medina is a true scholar and has been a source of trusted and valuable advice. Finally, I would like to thank Dr. Gentili for her thoughtful questions and kind support. I am also very grateful to Mr. Caesar Singh and the United States Department of Transportation for funding my education and research. The financial support has allowed me to attend school full-time and focus on the completion of this dissertation. Finally, I would like to thank my family for their endless patience and support during the long process of completing this degree. Work performed under this project has been partially supported by US DOT's Research and Innovative Technology Administration (RITA) award # RITARS-12-H-GMU (GMU #202717). DISCLAIMER: The views, opinions, findings and conclusions reflected in this presentation are the responsibility of the author(s) only and do not represent the official policy or position of the USDOT/RITA, or any State or other entity. iii Table of Contents Page List of Tables . vii List of Figures . viii Abstract . .x 1 Introduction . .1 1.1 Motivation and Problem Statement . .1 1.2 Traditional Flood Assessment . .1 1.3 Non-authoritative Data . .2 1.4 Scope of Dissertation . .4 1.5 Dissertation Organization . .5 2 Literature Review . .7 2.1 Disasters and Risk . .7 2.2 Technology as a Resource . .9 2.3 Flood Assessment . 13 2.3.1 Hydrologic modeling . 14 2.3.2 River stage/DEM . 15 2.3.3 Remote sensing . 15 2.4 Volunteered Geographic Information and Disasters . 17 2.5 Disaster and Transportation Analysis . 20 2.6 Data Fusion . 21 3 Methodology . 24 3.1 Geospatial Methods . 24 3.2 Data Fusion . 25 3.2.1 Overview . 25 3.2.2 Data pre-processing . 27 3.2.3 Data integration . 29 3.2.4 Road hazard map . 30 4 Application of Non-authoritative Data for Flood Estimation . 32 4.1 Data . 32 4.2 Data Analysis . 34 iv 4.2.1 Identification of flood extent . 34 4.2.2 Generation of flood hazard maps . 36 4.2.3 Ground data integration . 37 4.3 Results and Discussion . 38 4.3.1 Flood classification using DEM and river gauge data . 38 4.3.2 Flood classification using machine learning tree induction . 38 4.3.3 Flood hazard maps and ground data integration . 38 4.4 Conclusions . 43 5 Crowdsourced Data for Flood and Road Damage Assessment . 44 5.1 Data . 44 5.1.1 Non-authoritative data . 44 5.1.2 Authoritative data . 45 5.2 Data Analysis . 47 5.2.1 Non-authoritative damage assessment . 47 5.2.2 Integration with authoritative data . 47 5.2.3 Generation of road damage map . 48 5.3 Results and Discussion . 48 5.3.1 Damage assessment and authoritative data . 48 5.3.2 Road damage map . 53 5.4 Conclusions . 55 6 Real-Time Flood Assessment using Crowdsourced and Volunteered Data . 58 6.1 Data . 58 6.1.1 Authoritative data . 58 6.1.2 Non-authoritative data . 60 6.2 Data Analysis . 61 6.2.1 Data layer generation . 61 6.2.2 Data layer integration . 62 6.3 Results and Discussion . 63 6.3.1 Flood extent identified by authoritative sources . 63 6.3.2 Flood extent identified by non-authoritative sources . 64 6.3.3 Layer integration and generation of flood map . 65 6.4 Conclusions . 67 7 Time Series of Flood Extent using Non-authoritative Data . 68 7.1 Data . 68 7.1.1 Non-authoritative data . 68 7.1.2 Authoritative data . 69 v 7.2 Data Analysis . 71 7.2.1 Data layer generation . 71 7.2.2 Layer merge . 72 7.2.3 Prediction map . 73 7.3 Results and Discussion . 73 7.3.1 Flood determination using supervised classification . 73 7.3.2 Flood extent identified by SAR . 74 7.3.3 Flood classification using DEM and river gauge data . 75 7.3.4 Non-authoritative data layers . 75 7.3.5 Layer merge and flood extent estimation . 76 7.3.6 Road assessment . 81 7.4 Conclusions . 81 8 Discussion and Summary . 82 8.1 Non-authoritative Data Characteristics . 83 8.2 Model Characteristics . 84 8.3 Economic Viability . 86 8.4 Conclusions . 87 References . 88 vi List of Tables Table Page 4.1 Number and percentages of pixels classified as water. 41 5.1 Comparison between non-authoritative and authoritative data. 53 7.1 Data sources and availability for Calgary floods. 76 vii List of Figures Figure Page 1.1 Spectrum of confidence associated with authoritative and non-authoritative data sources. .3 2.1 Cumulative damage from floods in the United States (a) and globally (b) in US Dollars, 1900-2012. 10 3.1 Flowchart illustrating model for the fusion of remote sensing and non-authoritative data. 26 3.2 Layers generated from multiple sources of remote sensing, authoritative, and non-authoritative data. 28 4.1 Maximum daily precipitation rate and and accumulated precipitation for the period ranging from 1 April to 31 May 2011. 34 4.2 Digital Elevation Model of Memphis and the surrounding area. 35 4.3 Year 2011 water height profile for Mississippi River at Memphis, TN. 35 4.4 Water pixel classification using a digital elevation model (DEM) and Landsat data. 39 4.5 Flood hazard map indicating the probability of flood in percentage using DEM, Landsat, and ground data. 40 4.6 Histogram of pixels classified as water. 42 5.1 Crowsourced assessments for the Civil Air Patrol data . 46 5.2 Storm surge extent generated by FEMA and the locations of Civil Air Patrol photos and geolocated videos. ..
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